Robust Resampling Detection in Digital Images

Nguyễn, Hiếu Cường; Katzenbeisser, Stefan

doi:10.1007/978-3-642-32805-3_1

Cited by 13 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first one is that the detection rate dropped seriously when performing with a compressed image, since image compression weakens the resampling periodicity such images have. The second one is that the previous works mentioned above have the tendency to lose performance with specific images that include highly textured region since the traces of resampling are dependent on an image's own properties [11].…”

Section: Related Workmentioning

confidence: 99%

Enhanced resampling detection based on image correlation of 3D stereoscopic images

Choi

Hyun

Choi

et al. 2017

J Image Video Proc.

View full text Add to dashboard Cite

In this paper, we propose a resampling detection method for stereoscopic images. Although previous resampling techniques can be applied to stereoscopic images, performance improvement is hard to be expected with the two separated results. In this research, we found a strong relationship between the left and right images derived from the characteristics of the stereoscopic images. The proposed technique exploits that relationship of the stereoscopic images as additional information for reliable detection performance. Furthermore, the proposed method includes a preprocessing step to acquire the independent performance from the image's own characteristics. The experimental results exhibit superior performance compared with the existing works.

show abstract

Section: Related Workmentioning

confidence: 99%

Enhanced resampling detection based on image correlation of 3D stereoscopic images

Choi

Hyun

Choi

et al. 2017

J Image Video Proc.

View full text Add to dashboard Cite

show abstract

“…Based on [18] some improved techniques have been introduced in [19] and [20]. The author in [19] showed that the p-map of a re-sampled image is periodic and this periodicity does not depend on the prediction weights that are used to compute the correlations of neighboring pixels.…”

Section: Techniques For Image Re-sampling Detectionmentioning

confidence: 99%

“…Although the values of prediction weights do not affect the periodicity of the pmap in theory, the authors in [20] found that the selected set of predefined weights can strongly affect the obtained results: using one predefined set of weights for detection, peaks can be recognized in the transformed p-map, but using another set, peaks are not evident (though the periodicity exists in theory). Therefore, they use a predefined set, which is chosen through experimentation and apply the Radon transformation to the probability map of the analyzed image in order to enhance the frequency peaks and consequently the robustness of the overall technique.…”

Section: Techniques For Image Re-sampling Detectionmentioning

confidence: 99%

“…Therefore, they use a predefined set, which is chosen through experimentation and apply the Radon transformation to the probability map of the analyzed image in order to enhance the frequency peaks and consequently the robustness of the overall technique. An example for detection of (uncompressed) re-sampled images by using [20] is presented in Fig. 1.…”

Section: Techniques For Image Re-sampling Detectionmentioning

confidence: 99%

“…1. The techniques [16][17][18][19][20] work well for detecting traces of re-sampling in uncompressed images. However, they fail when applied to JPEG images.…”

Section: Techniques For Image Re-sampling Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Resized Double JPEG Compressed Images – Using Support Vector Machine

Nguyễn

Katzenbeisser

2013

Communications and Multimedia Security

Self Cite

View full text Add to dashboard Cite

Abstract. Since JPEG is the most widely used compression standard, detection of forgeries in JPEG images is necessary. In order to create a forged JPEG image, the image is usually loaded into a photo editing software, manipulated and then re-saved as JPEG. This yields to double JPEG compression artifacts, which can possibly reveal the forgery. Many techniques for the detection of double JPEG compressed images have been proposed. However, when the image is resized before the second compression step, the blocking artifacts of the first JPEG compression are destroyed. Therefore, most reported techniques for detecting double JPEG compression do not work for this case. In this paper, we propose a technique for detecting resized double JPEG compressed (called RD-JPEG) images. We first identify features that can discriminate RD-JPEG images from JPEG images and then use Support Vector Machines (SVM) as a classification tool. Experiments with many RD-JPEG images with different quality and scaling factors indicate that our technique works well.

show abstract

A Machine Learning Based Technique for Detecting Digital Image Resampling

Nguyễn

2016

Intelligent Information and Database Systems

View full text Add to dashboard Cite

Robust Resampling Detection in Digital Images

Cited by 13 publications

References 10 publications

Enhanced resampling detection based on image correlation of 3D stereoscopic images

Enhanced resampling detection based on image correlation of 3D stereoscopic images

Detecting Resized Double JPEG Compressed Images – Using Support Vector Machine

A Machine Learning Based Technique for Detecting Digital Image Resampling

Contact Info

Product

Resources

About